Putting error bars on density functional theory.

Autor: Yuk SF; Department of Chemistry and Life Science, United States Military Academy, West Point, NY, 10996, USA., Sargin I; Department of Metallurgical and Materials Engineering, Middle East Technical University, 06800, Ankara, Turkey., Meyer N; Physics Department, Stanford University, Stanford, CA, 94305, USA., Krogel JT; Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA., Beckman SP; School of Mechanical and Materials Engineering, Washington State University, Pullman, WA, 99164, USA., Cooper VR; Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA. coopervr@ornl.gov.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Aug 30; Vol. 14 (1), pp. 20219. Date of Electronic Publication: 2024 Aug 30.
DOI: 10.1038/s41598-024-69194-w
Abstrakt: Predicting the error in density functional theory (DFT) calculations due to the choice of exchange-correlation (XC) functional is crucial to the success of DFT, but currently, there are limited options to estimate this a priori. This is particularly important for high-throughput screening of new materials. In this work, the structure and elastic properties of binary and ternary oxides are computed using four XC functionals: LDA, PBE-GGA, PBEsol, and vdW-DF with C09 exchange. To analyze the systemic errors inherent to each XC functional, we employed materials informatics methods to predict the expected errors. The predicted errors were also used to better the DFT-predicted lattice parameters. Our results emphasize the link between the computed errors and the electron density and hybridization errors of a functional. In essence, these results provide "error bars" for choosing a functional for the creation of high-accuracy, high-throughput datasets as well as avenues for the development of XC functionals with enhanced performance, thereby enabling the accelerated discovery and design of new materials.
(© 2024. UT-Battelle, LLC and Irmak Sargin, Scott P. Beckman.)
Databáze: MEDLINE
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